I am a visiting scholar in the Department of Political Science at the University of Pittsburgh. I earned my Ph.D. in Public Policy from Seoul National University in June 2023, where I also completed my master's degree in Public Policy and my bachelor's degree in Law. My research interests are broad, primarily focusing on neo-institutional theory, agenda-setting theory, decision-making theory, and policy implementation theory. My Ph.D. thesis delved into the program budget system (a.k.a performance budgeting), revealing the determinants and consequences of its structural complexity as well as offering specific insights for public expenditure management. I was the winner of the best Ph.D. dissertation award of KAPA 2023. 

I am a policy scientist specializing in data science. I have successfully developed and implemented various statistical models and machine learning algorithms. These include traditional econometrics models (e.g., dynamic panel models, structural equation models), tree models (e.g., Random Forest, XGBoost) as well as neural network models (e.g., CNN, RNN, and Transformers). I published several articles employing those methods on structured and unstructured data in the fields of public administration, public policy, and public finance. A recent highlight in my work is an organizational diagnosis project aimed at optimizing personnel allocation; this involved both the classification of the task difficulty and the prediction of task duration based on administrative documents. My expertise extends across multiple programming languages and statistical software packages, notably SAS and Python. I have developed and maintained several SAS macro programs and Python packages. 

I am looking forward to collaborating with you. Please don't hesitate to contact me if you have any opportunity to offer. :)